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@f3kin
f3kin / codex-review-SKILL.md
Created June 1, 2026 03:02
codex-review — cross-model adversarial code review for Claude Code (day 1 of my Claude stack series)

name: codex-review description: > Cross-model code review using OpenAI Codex CLI or direct API fallback. Three modes: review (diff review), challenge (adversarial break-the-code), consult (ask anything with session continuity). Catches blind spots that Claude shares with itself. Use before merging risky PRs, after large refactors, or whenever a second opinion from a different model family helps. Trigger phrases: "codex review", "second opinion", "cross-model review", "hostile review", "challenge this code", "have codex look at this".

@hessammehr
hessammehr / pi-anthropic-pro-compat.sh
Last active June 1, 2026 12:46
You helped me write the gist at hessammehr/31af27c808502dcf53f5b1222dcbcaa1 (can grab it using gh) on a different computer. Can you grab, read and apply it (might need to adapt), then verify that it works?
#!/usr/bin/env bash
set -euo pipefail
# pi Anthropic Pro/Max subscription compatibility patch
#
# Purpose:
# Patch an installed @mariozechner/pi-coding-agent so provider=anthropic can
# use Anthropic Claude Pro/Max OAuth subscription routing, similar to Claude Code.
#
# Why this exists:
@MuhammadSaim
MuhammadSaim / rarreg.key
Last active June 1, 2026 12:44
Step 1: Create a file called rarreg.key Step 2: Paste into the file the raw content of this gist Step 3: Go to Winrar install directory (by default => c:\ProgramFiles\WinRAR\ ) Step 4: Paste the rarreg.key into WinRAR directory Step 5: Enjoy
RAR registration data
WinRAR
Unlimited Company License
UID=4b914fb772c8376bf571
6412212250f5711ad072cf351cfa39e2851192daf8a362681bbb1d
cd48da1d14d995f0bbf960fce6cb5ffde62890079861be57638717
7131ced835ed65cc743d9777f2ea71a8e32c7e593cf66794343565
b41bcf56929486b8bcdac33d50ecf773996052598f1f556defffbd
982fbe71e93df6b6346c37a3890f3c7edc65d7f5455470d13d1190
6e6fb824bcf25f155547b5fc41901ad58c0992f570be1cf5608ba9
@LaurieScheepers
LaurieScheepers / happi.md
Last active June 1, 2026 12:42
HAPPI/1.1 — Hypertext Auditable Provenance Protocol Interface. Single-file polyglot: valid Markdown + executable Bash + embedded stdlib Python runtime + JSON envelope + OpenAPI schema. The open audit-chain protocol for AI dispatch. Spec lives inside the implementation.
#!/usr/bin/env bash
: <<'HAPPI_DOC'
<!-- happi:label=frontmatter -->
# happi.md — HAPPI/1.1
> *"AI is a syscall. happi.md is the protocol."* — V>>--<<V
**Version**: `happi/1.1` — current runtime. Both `happi/1.0` and `happi/1.1` envelopes are accepted; emitted events carry the runtime's version (`happi/1.1`), not the envelope's. v1.1 adds the `idr` event type, the `flags.audit=true` opt-in, and three runtime cmds (`pr.reference`, `hypothesis.register`, `quine.spawn`).
**Format**: polyglot — one file, five parsers (Markdown, bash, embedded Python runtime, JSON envelope, OpenAPI YAML)
@LIUXVuse
LIUXVuse / llm-wiki.md
Created June 1, 2026 12:39 — forked from karpathy/llm-wiki.md
llm-wiki

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@docularxu
docularxu / blog-claude-code-china-zh.md
Last active June 1, 2026 08:58
在中国使用 Claude Code 解决 403 错误 - 完整指南(中文版)

在中国使用 Claude Code 解决 403 错误 - 完整指南(中文版)

在中国使用 Claude Code 解决 403 错误

2026 年 2 月 12 日

如果你在中国尝试使用 Claude Code,大概率会撞上 403 错误。本文覆盖三种使用场景的解决方案:

  • macOS 终端 (shell) - 在终端里直接使用 claude 命令行
  • VS Code 终端 - 在 VS Code 内置终端里使用 claude 命令行
@itsNisarg
itsNisarg / positional_encoding.py
Created June 1, 2026 12:25
Exploring positional embeddings
import torch
import matplotlib.pyplot as plt
# ── Environment diagnostics ───────────────────────────────────────────────────
# Print the runtime environment so results are reproducible and hardware-aware.
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"MPS available: {torch.backends.mps.is_available()}")
print(f"MPS built: {torch.backends.mps.is_built()}")

PCT Data Contract

This document specifies the data entities and columns that a data provider must deliver via the Plato Connector Target (PCT). Each entity corresponds to a CSV file pushed to S3.

General Requirements

  1. File Format: All files must be in CSV format with comma-separated values
  2. Encoding: UTF-8 encoding is required
  3. Header Row: The first row must contain column names exactly as specified below